Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "44" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 37 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 35 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459852 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.928887 | 1.453166 | -0.408174 | -0.780586 | 4.908420 | 1.251400 | 2.117985 | 0.485887 | 0.8340 | 0.8447 | 0.2288 | 4.835719 | 4.516603 |
| 2459851 | digital_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.865332 | 1.060810 | -0.224512 | -0.611234 | -0.369032 | -0.409323 | -0.128453 | -0.997750 | 0.7562 | 0.7565 | 0.3296 | 3.206028 | 3.170841 |
| 2459850 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.653715 | 1.763657 | 0.002736 | -0.594706 | 0.038439 | -0.661014 | -0.470547 | -0.837631 | 0.0557 | 0.0480 | 0.0038 | 1.189620 | 1.190050 |
| 2459849 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 9.785305 | 2.683291 | 1.343646 | -0.509069 | 8.956043 | -0.210257 | 19.584104 | 3.628425 | 0.0586 | 0.0511 | 0.0041 | 1.212571 | 1.211234 |
| 2459848 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.425168 | 2.808217 | 1.449197 | -0.681661 | 1.890105 | -0.112313 | 11.302603 | 4.970805 | 0.0592 | 0.0504 | 0.0028 | 1.224744 | 1.217713 |
| 2459847 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.941679 | 1.864717 | 1.490442 | -0.615777 | 13.094046 | 0.072344 | 81.454135 | 7.608559 | 0.0466 | 0.0446 | 0.0041 | 1.201114 | 1.200793 |
| 2459846 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 13.060742 | 1.468255 | 0.373165 | -0.599717 | 6.358430 | 0.280780 | 22.630268 | 2.930634 | 0.0655 | 0.0481 | 0.0025 | 1.218483 | 1.211393 |
| 2459845 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 15.773370 | 3.322278 | 1.465044 | 0.408462 | 11.951355 | 0.786401 | 46.175614 | 17.629941 | 0.7257 | 0.7638 | 0.3588 | 5.761107 | 6.933267 |
| 2459844 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 3.764925 | 0.492366 | 5.156471 | 3.661905 | 0.276942 | 0.101182 | 0.410799 | -0.632997 | 0.0255 | 0.0250 | 0.0006 | nan | nan |
| 2459843 | digital_maintenance | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 3.362164 | 2.557196 | 10.358821 | 9.517660 | 3.958690 | 4.027538 | 14.297383 | 9.130444 | 0.7558 | 0.7647 | 0.3671 | 4.109246 | 3.922218 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Variability | 4.908420 | 0.928887 | 1.453166 | -0.408174 | -0.780586 | 4.908420 | 1.251400 | 2.117985 | 0.485887 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Shape | 1.865332 | 1.865332 | 1.060810 | -0.224512 | -0.611234 | -0.369032 | -0.409323 | -0.128453 | -0.997750 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Shape | 2.653715 | 2.653715 | 1.763657 | 0.002736 | -0.594706 | 0.038439 | -0.661014 | -0.470547 | -0.837631 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 19.584104 | 9.785305 | 2.683291 | 1.343646 | -0.509069 | 8.956043 | -0.210257 | 19.584104 | 3.628425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 11.302603 | 2.808217 | 3.425168 | -0.681661 | 1.449197 | -0.112313 | 1.890105 | 4.970805 | 11.302603 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 81.454135 | 1.864717 | 23.941679 | -0.615777 | 1.490442 | 0.072344 | 13.094046 | 7.608559 | 81.454135 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 22.630268 | 13.060742 | 1.468255 | 0.373165 | -0.599717 | 6.358430 | 0.280780 | 22.630268 | 2.930634 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 46.175614 | 3.322278 | 15.773370 | 0.408462 | 1.465044 | 0.786401 | 11.951355 | 17.629941 | 46.175614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Power | 5.156471 | 3.764925 | 0.492366 | 5.156471 | 3.661905 | 0.276942 | 0.101182 | 0.410799 | -0.632997 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44 | N05 | digital_maintenance | ee Temporal Discontinuties | 14.297383 | 2.557196 | 3.362164 | 9.517660 | 10.358821 | 4.027538 | 3.958690 | 9.130444 | 14.297383 |